Challenges exist in detecting fine-grained personal attributes in surveillance videos. Existing approaches include using object detectors trained from large amounts of data using machine learning techniques. However, typical surveillance conditions (for example, low resolution images, pose and lighting variations) lead to cases where machine learning techniques fail because the attributes of interest cannot be reliably identified from images due to changes in appearance caused by the surveillance conditions (for example, shadows that look like beards, or eyeglasses that cannot be identified due to poor resolution).